In this paper, we use a variance-based genetic ensemble (VGE) of Neural Networks (NNs) to detect anomalies in the satellite's historical data. We use an efficient ensemble of the predictions from multiple Recurrent Neural Networks (RNNs) by leveraging each model's uncertainty level (variance). For prediction, each RNN is guided by a Genetic Algorithm (GA) which constructs the optimal structure for each RNN model. However, finding the model uncertainty level is challenging in many cases. Although the Bayesian NNs (BNNs)-based methods are popular for providing the confidence bound of the models, they cannot be employed in complex NN structures as they are computationally intractable. This paper uses the Monte Carlo (MC) dropout as an approximation version of BNNs. Then these uncertainty levels and each predictive model suggested by GA are used to generate a new model, which is then used for forecasting the TS and AD. Simulation results show that the forecasting and AD capability of the ensemble model outperforms existing approaches.

翻译：本文采用基于方差的遗传集成神经网络（VGE）方法，对卫星历史数据进行异常检测。通过利用每个模型的不确定性水平（方差），我们高效集成了多个循环神经网络（RNNs）的预测结果。在预测过程中，每个RNN由遗传算法（GA）引导，构建其最优结构。然而，在许多情况下，模型不确定性水平的计算具有挑战性。尽管基于贝叶斯神经网络（BNNs）的方法在提供模型置信区间方面较为常用，但由于其在复杂神经网络结构中计算不可行，难以实际应用。本文采用蒙特卡洛（MC）dropout作为BNN的近似方法，随后将GA提出的各预测模型及其不确定性水平相结合，生成新模型，用于时间序列预测与异常检测。仿真结果表明，该集成模型的预测与异常检测能力优于现有方法。